This invention is generally in the field of process control techniques, and relates to a method and system for controlling a process of manufacturing patterned structures, such as photolithography and etching processes.
The currently common methods for process control in photolithography, particularly micro-lithography, are based on the use of CD-SEM. The latter is a stand-alone tool, which performs measurements of critical dimensions (minimal lateral dimensions of a pattern) for creating Statistical Process Control (SPC) trend charts for further monitoring thereof. One of these methods involves creating a so-called xe2x80x9cFocus-Exposure Matrixxe2x80x9d (FEM), produced by varying the focus and exposure (energy) parameters of the lithography from field to field within the wafer, thereby producing a two-dimensional array of fields spanning a range of these parameters. By determining CD in each of the FEM fields, optimal values of the focus and exposure, as well as their allowed tolerance (process window), are determined for each specific process.
Recently, tools based on scatterometry have been developed, which provide for higher accuracy and repeatability, faster measurement, smaller volume and lower cost, as compared to CD-SEM tools. Such scatterometry-based tools are disclosed, for example, in U.S. Pat. Nos. 5,867,276 and 5,963,329; and in the following publication: xe2x80x9cSpecular Spectroscopic Scatterometry in DUV Lithographyxe2x80x9d, Xinhui Niu et al, SPIE Vol. 3677, SPE Conference on Metrology, Inspection and Process Control for Microlithography XIII, pp. 159-168.
Scatterometry is a method by which the optical signature (spectral response) of a periodic structure is measured. The signature can be obtained by measuring the optical properties of a structure (reflectance or ellipsometric parameters) as a function of one or more light parameters, e.g., the angle of incidence, polarization or wavelength. Due to the periodicity of the structure, it is possible to theoretically calculate the signature of a given sample using exact models thereof (e.g., utilizing a Rigorous Couple Wave Theory (RCWT)). Processing is thus performed by correlating the measured signature to theoretically calculated signatures, while fitting the structure""s parameters. This fitting method suffers from such drawbacks as long calculation time, in-adequacy to real-time calculations, and the need for detailed knowledge about the structure (e.g., optical constants) that is required as input to the model. The problem of long calculation time is usually overcome by preparing a library of pre-calculated signatures. This procedure, however, requires a long setup time. The detailed knowledge about the structure, in many cases, also requires preliminary setup processes, such as material characterization. Additionally, the measurement is limited to periodic structures that do not usually exist within the die, thus requiring fabricating special test structures and correlating the measurements on these test structures to measurements taken within the die. Yet another problem is the complicated, sometimes indirect relation between the process parameters (e.g., focus and exposure) and the profile parameters, rendering the attempt to control the process by modifying process parameters based on profile information, which is difficult to implement. These problems impede the application of scatterometry-based systems as a production tool, specifically for integrated monitoring that require a fast feedback for process control.
According to another technique, disclosed in the article xe2x80x9cPhi-Scatterometry for On-line Process Controlxe2x80x9d, N. Benesch et al, AEC/APC Symposium XII, Lake Tahoe, Nev., USA, Sep. 23-28, 2000, the signatures measured in different fields of a Focus-Exposure Matrix are classified using a neural network (NN) under those found within the control limits and those found outside of them. In other words, this technique provides only xe2x80x9cpassxe2x80x9d/xe2x80x9cfailxe2x80x9d information which allows a Process Alarm to be operated. However, no quantitative information is provided, therefore feedback to the process (adjusting the working parameters of the processing tool in a closed loop control) is impossible.
There is accordingly a need in the art to facilitate the control of a process of manufacturing patterned structures, particularly micro-lithography, by providing a novel control method and system. The present invention introduces a methodology that starts with identifying those major process parameters whose variation affects the process results. This new methodology also directly exploits the dependence of the measured signature on the process parameters, without requiring any model having predictive capabilities with regard to the way this dependence is manifested.
The invention is particularly useful for controlling a lithography process, wherein focus and exposure are among the dominant factors affecting the lithographical profile (critical dimensions, wall angle, etc.). These parameters are usually considered in order to control the lithography process and keep the resulting profile within the required control limits. The new methodology bypasses the main limitations inherent in conventional scatterometry as presented above.
In the description below, the following terms as used:
The term xe2x80x9cparametric matrixxe2x80x9d or in short xe2x80x9cmatrixxe2x80x9d used herein signifies a set of patterned structures (wafers) and/or fields created within the structure(s), that were fabricated using different values of one or more working parameters of the process to be controlled. Consequently, the term xe2x80x9cmatrix fieldxe2x80x9d or in short xe2x80x9cfieldxe2x80x9d signifies one specific part of a parametric matrix, being a wafer or part of a wafer, having a specific value or set of values of the working parameters. All fields are supposed to include equivalent measurement sites, not necessarily in the same locations.
The term xe2x80x9cmeasurement sitexe2x80x9d or in short xe2x80x9csitexe2x80x9d refers to a specific location found within each matrix field where the signature measurement is actually being taken.
The term xe2x80x9csignaturexe2x80x9d signifies an optical response of the structure to predetermined incident light. Such a signature may be measured as a diffraction of light interacting with the structure as a function of a light parameter such as wavelength (spectrum), angle of incidence, ellipsometry, etc. The term xe2x80x9csignaturexe2x80x9d refers to the total optical information that can be attained from a certain field, including several measurements taken at different measurement conditions, different measurement tools and/or at different measurement sites within the same field.
The term xe2x80x9creference tool resultsxe2x80x9d signifies the results of one or more measurements applied to the parametric matrix or a part thereof by reference tools other than the measurement apparatus of the present invention.
The term xe2x80x9creference dataxe2x80x9d refers to all data available in order to perform the setup process (training of the NN), including mainly but not only signatures measured on a group of matrix fields and reference tool results from corresponding fields, as well as the processing conditions of the same field and any other sort of information available on these fields.
The term xe2x80x9ccontrol windowxe2x80x9d or xe2x80x9cprocess windowxe2x80x9d signifies a range or ranges of one or more working (process) parameters providing desired process results.
The term xe2x80x9cmerit functionxe2x80x9d refers to a function that gets two signatures as input and results with a single number that is some measure of the xe2x80x9cdistancexe2x80x9d between the two input signatures.
There is thus provided according to one aspect of the present invention, a method of controlling a process to be applied to a patterned structure in a production run, the method comprising the steps of:
(i) providing reference data including data representative of diffraction signatures corresponding to a group of different fields in a structure similar to said patterned structure in the production line, and data representative of a control window for the process parameters corresponding to a signature representative of desired process results, said group of different fields being characterized by different process parameters used in the manufacture of these fields;
(ii) providing an expert system trained to be responsive to input data representative of a diffraction signature to provide output data representative of corresponding effective parameters of the process;
(iii) applying optical measurements to at least one site on said patterned structure in the production line to obtain at least one diffraction signature of said patterned structure in the production line and generate data representative thereof;
(iv) supplying the generated data to said expert system, which analyses the data to thereby determine effective parameters of the process applied to said patterned structure in the production line; and
(v) analyzing said effective process parameters to determine deviation thereof from corresponding nominal values to thereby enable the process control.
The reference data is created during an off-line operational mode (calibration procedure) consisting of the following. The process to be controlled is applied to different fields on a test structure (wafer) or to different test structures, utilizing different values of one or more working parameters of the process, thereby preparing a parametric matrix including variations of at least one working parameter. When dealing with a lithography process, such a parametric matrix is typically an F-E matrix (FEM). The FEM is printed by using the same or similar mask as that used in the production run, varying the exposure along one axis and the focus along the other axis of a two-dimensional field array. Then, measurements are applied to the test wafer(s) in order to determine the signatures corresponding to a group of different fields.
Optionally, additional measurements are applied to the same field using reference tools (e.g. CD-SEM, Cross Sectional SEM, AFM) and their results are added to the reference data. For example, when dealing with a lithography process, CD-SEM values measured on the same F-E matrix fields may be used as reference tool results. The setup process is finalized by using the entire reference data as a training set for training an expert system, e.g., an artificial neural network (NN). In this stage, the NN is trained to return the process parameters and any available reference tool results upon receiving signatures as input. Once the NN is properly trained to do this with the training set data, i.e., with the reference data, it will usually also be able to find the correct process parameters when inputting a new signature.
During an on-line operational mode (real-time measurements during a production run), optical signatures are measured at different sites on the real structure, and supplied as input to the NN. The NN then outputs the effective process parameters corresponding to each signature. Closed loop process control can than be performed using the deviations of the effective process parameters from their corresponding nominal values. For example, in the lithography case, if measured signatures consistently show that the effective exposure is lower than the nominal one, correction may be applied to the process by means of increasing the exposure value. This process control method thus overcomes one of the problems of conventional scatterometry: eliminates the need to explicitly translate a change in profile parameters into a required change in process parameters, since the required change in process parameter is a direct output of the method of the present invention. In fact, with respect to the present example of the lithography process, even if the source of the profile change is other than the exposure, the profile may still be accurately corrected by changing the exposure. The nature of the closed-loop-control may include different methods such as feedback, feed-forward, etc.
Preferably, the method of the present invention also includes re-training of the NN. As described above, a set of fields is used in order to prepare the reference data by spanning the process window in several selected process parameters. In the lithography example, the use of a single wafer allows spanning the process window both in exposure and focus. However, there are many other process parameters that are assumed to be constant and are not actively varied throughout the selected set of fields, so-called xe2x80x9chidden parametersxe2x80x9d. In the lithography example, when using a single FEM wafer, film thickness for all the stack films are hidden parameters.
The values of hidden parameters may later on be changed, thereby affecting the diffraction signature in a manner that was not taken into consideration when training the NN. This introduces errors into the NN results. One way to eliminate the effect of hidden parameters is to xe2x80x9cunhidexe2x80x9d them, i.e., to expand the matrix so as to include the hidden parameters as active parameters of the matrix. For example, photoresist film thickness may become a matrix parameter by producing several FEM wafers having deliberately different film thicknesses. By applying measurements to all wafers and including all the measurement results into the reference data used to train the NN, the effect of the variable film thickness may be taken into account in the same way as focus and exposure parameters.
There are two different ways to treat the expanded matrix. According to one embodiment of the method, a new parameter (photoresist film thickness in the above example) should be a control parameter to be treated on equal footing as the other parameters. To this end, the value of the new parameter must be known at each field and added to the reference data. In the lithography example, each wafer has to be measured for its film thickness and the NN has to be trained to output F, E and film thickness values. However, it is also possible to train the NN with all the field signatures, regardless of which wafer they come from, without requiring the NN to output the new matrix parameter(s) (film thickness). In this case, the NN will learn how to provide the correct values for the existing matrix parameters (F and E) regardless of the values of the new matrix parameter(s) (film thickness). Since their exact values are not required, the values for the new parameters (film thickness) do not need to be measured in this case. Therefore, in this case, the effect of the expanded matrix consists of xe2x80x9cimmunizingxe2x80x9d the NN from possible errors coming from variations in the new parameter(s) (film thickness). Such additional parameters that are sampled but not controlled will be referred to as xe2x80x9csampled parameterxe2x80x9d, in contrast to the xe2x80x9ccontrol parametersxe2x80x9d.
It should be noted that expanding the matrix means preparing and measuring additional wafers, resulting in an increase in setup time and effort. If, for example, there are n sampled parameters and each of them needs be sampled m times across their corresponding allowed ranges, then the expanded matrix size will be mn times the size of original matrix, including only the control parameters, making the expanded matrix impractical in many cases. To partly overcome this problem, a xe2x80x9csparsely expanded matrixxe2x80x9d can be used in which the sampled parameters are not fully sampled for all possible cases, but rather sampled sparsely. If, for example, there are two sampled parameters, and each needs in principle to be sampled 5 times, then instead of multiplying the matrix size by 52=15, a sparse sampling of as few as 5-9 cases may supply most of the required information in order to immune the system from variations in these two parameters.
An effective way to immune the system to possible errors is to use the naturally occurring distribution of the hidden parameters. By randomly choosing a group of fields with varied production conditions (e.g., coming from different wafers, different lots, different tools or different time), the reference set may naturally be populated with fields having the correct distribution in all hidden parameters. Such a training set may allows the NN immunization to take place without actively sampling a large number of hidden parameters (which may even be unknown) and without the need to produce and measure a large number of fields.
One specifically attractive way to sample the naturally occurring distribution is to utilize wafers that are anyhow produced for regular periodical tests. For example, FEM wafers are routinely produced (in many cases, daily) in order to verify the stability of the production process and follow variations. Thus, by adding each time (day) additional information about new wafers, it is possible to improve the immunity of the system with time.
Additionally, the method of the present invention may include immunizing the NN to possible machine errors that are common in different measurement tools. If, for example, the measurement tool may have gain and offset errors, it is possible to immune the system to such errors by simulating them. In this case, after measuring a set of signatures for a set of fields, machine errors are simulated by artificially adding bias and gain factors to the signatures. Each signature may be thus duplicated several times, applying to different duplicates different amounts of gain and offset, thus producing additional sampled parameters to the matrix.
A set of signatures measured on the parametric matrix can be used as a signature library (look-up-table), which may be part of the reference data. According to this embodiment, during production run, every measured signature is compared to the signatures stored in the signature library, while searching for the signature that is the closest to the measured one. The search is carried out using a xe2x80x9cmerit functionxe2x80x9d that measures the level of fitting between any two signatures.
The signature library can also be used for verification when using an NN for the fitting process. Verification is needed, since the NN-based method, as described above, has no internal way of measuring how successful the interpretation is, i.e., what reliability the user should attribute to the results. Verification may be done in one of the following ways:
1. Searching the signature library and comparing the NN results to the search results: if the results are sufficiently close in the control parameters space, then the NN result is assigned a high reliability score;
2. Looking in the signature library for the signature whose xe2x80x9ccoordinatesxe2x80x9d in the control parameters"" space are the closest ones to the NN result and comparing this library signature with the newly measured signature: if the merit function between the library signature and the new signature is sufficiently low, the NN result gets a high reliability score.
The data collection and analysis technique of the present invention enables to overcome most of the inherent disadvantages of conventional scatterometry, namely:
The setup of the present invention is simpler and requires neither deep understanding of the application nor long calculations. The setup may therefore be used for any structure, regardless of the complexity of underlying layers or the structure of the profiles, without the need to find the optical characteristics of the material involved. It may also be easily used by operators having minimal training.
The technique of the present invention can be applied to any measurement site, and not only to line gratings. Among the possible sites may be, for example, hole arrays, memory cells or any other diffracting structure. The only desired conditions are as follows: (1) the measured signal should not strongly depend on the exact measurement location, within the positioning accuracy of the system, and (2) light diffraction should be sufficiently strong to make the optical signal sensitive to the process parameters.
Real time measurement is very fast, regardless of the application complexity
Close loop control is directly available, as the output is already given in terms of the control parameters.
It should also be noted that, in distinction to the prior art classification method utilizing a learning system, the method of the present invention allows quantitative process control, while the classification technique allows solely process alarm. This is due to the fact that the output obtained with the invented method is continuous, while the only output obtainable with the prior art-technique is indicative of whether the profile is within the process window or not. Additionally, the method of the present invention has several mechanisms allowing it to be immune to different naturally-occurring variations that may affect the interpretation, making it more robust.
According to another aspect of the present invention, there is provided a production line for manufacturing patterned structures in a production run comprising:
(a) a processing tools arrangement characterized by certain values of its working parameters; and
(b) an optical measurement apparatus operable to apply a measurement to the structure and detect a diffraction signature indicative of light response of the structure to incident light, said diffraction signature varying with a change in at least one of said working parameters; and
(c) an expert system trained to be responsive to input data representative of a diffraction signature to provide output data representative of corresponding effective value of said at least one working parameter, thereby enabling analysis of said effective value to determine deviation thereof from a corresponding nominal value and allow control of said process.